Recommendations are an increasingly important merchandising tool in electronic commerce. For example, a customer of an online store may be presented with various product recommendations based upon order history, browsing history, demographic information, time of day, and/or a variety of other factors. Purchases by other users and the characteristics of the purchasing users may be employed to create a recommendation model based on the concept that a user is likely to purchase products that have been purchased by similarly situated users. Such an approach is termed collaborative filtering. Although recommendations may be created based upon various input data, all users are different and a high scoring recommendation may miss the mark. In addition, users may decide to purchase non-recommended products.